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802.11 Power-Saving Mode for Mobile Computing inWi-Fi hotspots:Limitations,Enhancements and Open IssuesG.Anastasia,M.Contib,E.Gregorib,A.Passarellab,∗Pervasive Computing & Networking Laboratory (PerLab)aDept.of Information Engineering,University of PisaVia Diotisalvi 2 - 56122 Pisa,Italyg.anastasi@iet.unipi.itbCNR - IIT InstituteVia G.Moruzzi,1 - 56124 Pisa,Italy{marco.conti,enrico.gregori,andrea.passarella}@iit.cnr.itAbstract.Nowadays Wi-Fi is the most mature technology for wireless-Internet access.Despite the large (and everincreasing) diffusion of Wi-Fi hotspots,energy limitations of mobile devices are still an issue.To deal with this,thestandard 802.11 includes a Power-Saving Mode (PSM),but not much attention has been devoted by the researchcommunity to understand its performance in depth.We think that this paper contributes to ﬁll the gap.We focuson a typical Wi-Fi hotspot scenario,and assess the dependence of the PSM behavior on several key parameterssuch as the packet loss probability,the Round Trip Time,the number of users within the hotspot.We show thatduring trafﬁc bursts PSM is able to save up to 90% of the energy spent when no energy management is used,andintroduces a limited additional delay.Unfortunately,in the case of long inactivity periods between bursts,PSMis not the optimal solution for energy management.We thus propose a very simple Cross-Layer Energy Manager(XEM) that dynamically tunes its energy-saving strategy depending on the application behavior and key networkparameters.XEM does not require any modiﬁcation to the applications or to the 802.11 standard,and can thusbe easily integrated in current Wi-Fi devices.Depending on the network trafﬁc pattern,XEM reduces the energyconsumption of an additional 20 −96%with respect to the standard PSM.Keywords:802.11,Wi-Fi,Power-Saving Mode,Network Architecture & Design,Mobile Computing,NetworkProtocols,Performance of Systems1 IntroductionSince the introduction of the 802.11 standard in 1997,802.11 wireless LANs (also known as Wi-Fi hotspots) havebecome more and more popular.Installations of Wi-Fi hotspots are nowadays very frequent,for example in companyand education buildings,coffee shops,airports,and so on.Figure1shows a simple Wi-Fi installation,where userscarrying mobile hosts (e.g.,laptops,PDAs,...) exploit an Access Point to connect to legacy Internet services.This isthe scenario used in the paper.Despite its increasing popularity,Wi-Fi still presents several problems that are far to be solved.One of themost important is the energy consumption of 802.11 wireless interfaces.Wireless cards have shown to account forabout 10% of the total energy consumption in current laptops [1,7].This percentage grows up to 50% in hand-held devices [1,31],and even beyond in smaller form-factor prototypes [41].Even worse,the difference betweenbattery capacities and the requirements of electronic components is expected to increase in the near future [40].Energy management is hence a core enabling factor for the Wi-Fi technology1.∗This work has been carried out while A.Passarella was with the Department of Information Engineering of the University of Pisa.1In this paper we talk about “energy management” instead of “power management”,though the latter keyword is quite more diffused in theliterature.Actually,this paper is not about optimizing the power consumption of 802.11,i.e.,by adjusting the transmission power or the receiversensitiveness.Rather,it is about optimizing the time intervals spent by the wireless interface in the different 802.11 power modes,in order tominimize the energy consumed to performnetworking activities.1INTERNETmobilehostsaccesspointfixedhostFigure 1:Wi-Fi hotspot scenario.The 802.11 standard deﬁnes a Power-Saving Mode (PSM),aimed at reducing the energy consumption of mobiledevices.Recently,several works have been devoted to highlight PSM limitations and propose enhancements.Themost closely related to our work are STPM [1],BSD [30] and SPSM [38] (we provide a comprehensive survey ofthe related work in Section2).These works highlight that PSM adds high transfer delays in a range of applicationand network conﬁgurations.Besides decreasing the user QoS,this might even increase the energy consumption ofthe device as a whole,with respect to the case when PSM is not used:the energy saved on the wireless interfaceby PSMgets overwhelmed by the energy spent by the rest of the mobile device during the additional transfer time.In this paper we focus on a different – yet very popular – scenario (thoroughly described in Section3) where thePSM additional delay is fairly limited.In Section4we show that using PSM in this scenario is an advisable choice.Thus,in Sections5and6we extensively characterize the PSM performance for a wide range of key parameters.To the best of our knowledge,this is the ﬁrst work providing such a detailed PSM analysis.We show that PSMis very effective during trafﬁc burst.With respect to the case when it is not used,PSM is able to save up to 90%of the energy required to download a burst.However,PSM is not quite ﬁt to deal with long User Think Timesbetween bursts,that can actually represent the main source of energy consumption.From this standpoint,theoriginal contribution of our work consists in a deep exploration on how to further reduce the energy consumptionduring User Think Times.Speciﬁcally,in Section7we deﬁne a Cross-Layer Energy Manager (XEM) that exploitsinformation scattered across several layers in the protocol stack to detect the beginning of User Think Times andbursts.During bursts XEMactivates PSM,while during User Think Times it switches the wireless interface off.XEMdoes not degrade the user QoS,and achieves additional energy saving with respect to the standard PSM between20% and 90%,depending on the User Think Time length,and the bursts’ size.In this work we provide two contributions.In the ﬁrst part of the paper,we provide an accurate model of the802.11 PSM,and deeply characterize the 802.11 PSM performance.In comparison with existing works,whichusually highlight scenarios in which PSMis not effective,we showthat there is a broad range of cases in which PSMcan be successfully used to reduce the energy consumption.In the second part of the paper,we turn to analyzePSM inefﬁciencies,and propose and evaluate XEM.With respect to existing work,XEM smoothly integrates withcurrent 802.11 PSMand does not require any modiﬁcation of legacy protocols and applications.XEMis thus a verylightweight,yet efﬁcient,solution to improve PSMin cases in which it is not efﬁcient.2 Related workUnderstanding and enhancing the performance of wireless LANs,mainly in terms of energy saving,has deservedincreased attention in the last few years.Papers in this ﬁeld can be divided into two main categories.Some workshighlight limitations of PSMand propose possible enhancements.Other works propose energy-management policiesthat are not speciﬁcally tailored to 802.11 but can be applied to this technology,as well.For ease of reading,in thefollowing of this section we follow the above classiﬁcation.For the sake of space,and because the environment is2signiﬁcantly different,we do not survey the broad research area on energy management for ad hoc networks.2.1 Energy-management policies for Wi-Fi hotspotsA pioneering work on this topic is presented by Krashinsky and Balakrishnan in [30].They carry out a simulationanalysis of PSM in presence of Web-browsing trafﬁc.In particular,they consider a single mobile user (i.e.,nocontention) inside the hotspot.The authors of [30] show that PSM can save around 90% of the energy spent bythe wireless interface at the cost of highly increased delay in the Web-page downloads.To cope with this problem,they propose the Bounded Slowdown Protocol (BSD).In BSD,the mobile host listens the Access Point Beaconswith decreasing frequency during idle times,to be mostly sleeping during User Think Times.BSD trades off energyconsumption for lower additional delays.Speciﬁcally,if the maximum acceptable delay is very low,BSD actuallyconsumes more energy than PSM.Therefore,BSD can be more or less suitable than PSM,if the additional delay orthe energy consumption deserves more importance for the user.As noted in the paper,BSD focuses on a scenariowhere PSM tremendously increases the transfer delay,and thus it represents a very effective solution.However,inour scenario this additional delay is quite limited.It should be noted that,thanks to its ﬂexibility,our Cross-LayerPower Manager (XEM) is able to use either PSMor BSDduring bursts.In contrast to BSD,XEMswitches the wirelessinterface off during User Think Times.As discussed in Section6.3,the XEMability to distinguish interarrival times(for which it is more convenient using the sleep state) from User Think Times (for which it is better to switch thewireless interface off) grants greater energy saving.More recently,Qiao and Shin proposed the Smart Power-Saving Mode (SPMS) [38].SPMS can be seen as a BSDenhancement.During an idle time,BSD deﬁnes statically the set of points in time where the mobile host listensfor Access Point Beacons.Instead,SPMS deﬁnes this set of points dynamically,based on an estimate of the idletime duration.SPMS is more energy efﬁcient than BSD,and still achieves the same performance in bounding theadditional delay.However,it still consumes more energy than PSM in some cases,and just exploits the sleep stateof the wireless interface to conserve energy.Since SPMS is close to BSD in spirit,the same remarks discussed aboveapply to SPMS,as well.The authors of [34] propose the Dynamic Beacon Period algorithm (DBP).As BSD and SPSM,DBP aims atreducing the additional delay introduced by PSM to Web-page download times.Basically,each mobile host selectsits own Beacon Interval,and the Access Point is responsible for generating (custom) Beacon frames for each mobilehost.Several scalability issues,that are key points to fairly evaluate DBP,are not addressed in [34].As in the casesof BSD and SPMS,DBP just exploits the sleep state of the wireless interface to conserve energy,for any kind of idletime that might occur.Anand et al.,[1] carry out an experimental evaluation of PSM both on PDAs and laptops.They primarily focuson the trafﬁc generated by applications using network ﬁle systems such as NFS and Coda.Their results conﬁrmtheconclusions in [30],as far as the additional delay introduced by the PSM.To overcome this problem,they proposethe Self-Tuning Power Management (STPM) protocol.STPM operates at the Operating System level,and exploitshints provided by the network applications.Essentially,hints describe the near future requirements of applicationsin terms of networking activities.STPM exploits these hints,and the energy characteristics of the entire system,tomanage the wireless interface appropriately.When these hints are not available,STPMestimates the trafﬁc patternsby spooﬁng it.Like STPM,our Cross-Layer Energy Manager sits on top of different energy management policies,and dynamically chooses the most appropriate one.The main difference between [1] and our work is that XEMis simpler,and never requires collaboration from the applications,i.e.,no modiﬁcations of the application code isrequired.Again,[1] focuses on a scenario where PSMdelays are a big problem,while in our scenario they are not.Finally,[9,39] propose energy-management policies for 802.11 WLANthat are orthogonal to the work presentedin this paper,and hence can coexist with XEM.32.2 Energy-management policies for generic wireless LANsOther works face the energy-management problem in WLAN environments,but do not focus on a speciﬁc wirelesstechnology.The authors of [32] propose a solution entirely centralized at the Access Point.Time is divided inBeacon Intervals (as in the standard 802.11),and – at the beginning of each Beacon Interval – the Access Pointcomputes a schedule for transmitting frames during the coming Beacon Interval.Before any other transmission,theAccess Point broadcasts a Beacon Frame to publicize which mobile hosts are going to receive frames.These mobilehosts remain awake until they have received all the scheduled frames,while the other hosts can immediately switchto a low-power mode.This solution gives to the Access Point the ﬂexibility of implementing several schedulingpolicies,but requires i) signiﬁcant computational burden at the Access Point,and ii) non-trivial modiﬁcations to the802.11 standard.The authors of [6] design an energy manager tailored exclusively to Web-based applications.By means ofprefetch-like techniques,Web pages are transferred over the WLAN in a single (or few) burst,thus maximizing theamount of time during which the wireless interface is switched off.This technique does not introduce signiﬁcantadditional delays.Of course,the energy manager is tied with the particular application it is designed for.In [3] it isshown that this constraint can be relaxed with an acceptable degradation of the energetic performance.Speciﬁcally,[3] dynamically estimates the expected duration of idle times.The mobile host is switched off for the (predicted)duration of the idle time.The work in [6] and [3] inspired some ideas on how User Think Times and new burstscan be detected.However,XEM fully exploits PSM when appropriate,and can avoid relying on application-levelinformation.The works in [31,44,46] use inactivity timeouts to decide when to switch off the wireless interface.Timeoutvalues are ﬁxed,and depend on the speciﬁc application.[31] relies on an Indirect-TCP architecture and buffers atthe Access Point packets arriving while the mobile host is disconnected.Instead,[44] avoids any support fromtheAccess Point,and exploits knowledge of the application behavior to avoid missing packets.Also [46] uses a pureclient-centric approach,i.e.,no support fromthe Access Point is exploited.Speciﬁcally,[46] uses an approach verysimilar to [3],in the sense that interarrival times are estimated on-line.Furthermore,inactivity timeouts are usedto detect User Think Times.With respect to [3],no support fromthe Access Point is exploited.Hence,packets thatmay arrive while the mobile host is disconnected are lost.Inactivity timeouts are also used by XEM.However,inour systemthey are dynamically adjusted based on the status of the network path.The works in [33,17,42] advocate energy management at the operating system level.[33] exploits on-lineapplication-level hints to decide when to shut down the wireless network.Hence,this systemrequires modiﬁcationsto the application code.The authors of [17,42] formulate the energy-management problem as a linear program,where the objective is minimizing the energy consumption of a particular component,and the maximum tolerableperformance degradation (for example in terms of additional delay) is the constraint.Then,they derive optimalenergy management policies to drive the component in the different operating modes.The main drawback of thisapproach is that it requires a-priori statistical models of the component usage.This information is not required byXEM.Finally,other approaches to energy management include transmission power control techniques [22],or adrastic re-design of the application-level architecture [37,24,25].Speciﬁcally,[25] introduces a quite recenttechnology,named AJAX.The main idea is decoupling (in Web-like applications) the user and the server via aproxy-based component (called Ajax engine) running on the client.The user actually interacts with the Ajax engine,that asynchronously fetch fromthe server the data required to fulﬁll the user requests.Ajax is able to signiﬁcantlyreduce the amount of data exchanged over the network in case of slight modiﬁcation of a currently-rendered Webpage.In general,application-level techniques are orthogonal to XEM.In the case of AJAX,the most straightforwardinteraction we can see is using XEMbelowAJAX.XEMwould interpret the trafﬁc pattern generated by AJAX (insteadof that generated by the user),and manage the wireless interface accordingly.4INTERNETmobilehostsaccesspointfixedhost(a) Wi-Fi hotspot scenariodownload intervalTkUTTUser Think TimesNBRnumber of bursts = number of UTTs =fixedhostkdburststaggedmobile hosttt(b) Application-level trafﬁcFigure 2:The reference environment.3 Detailed Scenario and Evaluation Methodology3.1 Reference ScenarioIn our analysis,we consider the typical Wi-Fi hotspot scenario,depicted in Figure1and replicated in Figure2(a)for the reader convenience,in which a mobile user accesses the Internet through an Access Point.We focus onbest-effort Internet applications,such as Web browsing,e-mail,ﬁle transfer (hereafter referred to as reference ap-plications).This choice is motivated by the evidence that the trafﬁc generated by these applications representsthe lion’s share of the today Internet trafﬁc,and they are very likely to be the dominant applications also in thenear-future Internet [10].Figure2(b) shows a snapshot of the typical trafﬁc generated by the reference applications.A tagged mobilehost downloads a predeﬁned number of bursts (NBR) froma ﬁxed server connected to the Internet.The downloadof two consecutive bursts is separated by a User Think Time (UTT) during which no trafﬁc ﬂows between theserver and the mobile host (the other details shown in the Figure are related to the PSM model,and will be thusexplained in Section5).Though very simple,this trafﬁc model captures the typical user behavior for non real-timeapplications.For example,Web users download a page (i.e.,a burst) and then read the page contents withoutgenerating any trafﬁc on the network.By considering several such downloads (say,NBR) from the same site,wemodel the behavior of a user navigating a single Web site for a while.Because of its importance,some conceptsof the paper are presented by using the Web as the reference application.However,the trafﬁc model is generalenough to represent also other best-effort applications,as well.Furthermore,we investigated a broad range of thescenario parameters when evaluating PSMand XEM.Therefore,we believe our results are not valid only in the Webcase.Furthermore,as far as the XEM deﬁnition,it is not heavily tied to the Web case,and can work with differentapplications,as discussed in Section7.4.We assume that the mobile host communicates with the ﬁxed server through a standard TCP-Reno (withoutdelayed acks [45]) connection.We also assume that consecutive bursts be downloaded over the same connection.In the Web case this corresponds to using the persistent-connection option deﬁned by HTTP/1.1 [27].Since HTTPﬁle transfers usually consist of few KB [13,20,21] this option was deﬁned to avoid the huge overhead of openinga new TCP connection for each ﬁle transfer.It reduces download times,and allows TCP to precisely learn thepath congestion.In Section4we highlight that this option has further advantages when PSM is used.Speciﬁcally,the additional delay introduced by PSM to TCP transfers becomes fairly small,and it does not signiﬁcantly impacton the energy consumption of the device as a whole.Thus,using persistent connections is a good idea for theother reference applications,as well.We ﬁnally assume that the mobile host does not utilize parallel concurrentconnections to download bursts.This is aligned with the suggestions of [27] when persistent connections are used,5and it makes the analysis of both PSM and XEM simpler.In Section7.4we highlight how XEM can be extendedto work in the case of concurrent TCP connections.One might argue that anyway the legacy TCP/IP architectureexhibits poor performance in a WLAN environment,both in terms of throughput and energy consumption [12].However,TCP/IP is currently the only off-the-shelf solution for Wi-Fi hotspots and thus our environment is similarto real-world WLAN installations.In our scenario,the hotspot is populated by other N (background) mobile hosts in addition to the tagged mobilehost.We assume that,at each point in time,M mobile hosts out of N are active,i.e.,they have a frame readyto be sent.As discussed in Section6.2.3,by varying the number of active mobile hosts (i.e.,M) we can analyzethe sensitiveness of PSM to the contention level in the hotspot,and – therefore – its scalability with respect to thenumber of users sharing the same Access Point.3.2 802.11 Power-Saving Mode (PSM)As a signiﬁcant part of this work is devoted to analyze the PSM performance,in this section we brieﬂy recall themain features of this algorithm.The interested reader is referred to the IEEE 802.11 standard for a completedescription [29].The objective of the 802.11 PSM is to let the wireless interface of a mobile host in the activemode only for the time necessary to exchange data,and turn it in sleep mode whenever it becomes idle.In a Wi-Fihotspot,this is achieved by exploiting the central role of the Access Point.Each mobile host within the hotspot letsthe Access Point know whether it utilizes the PSM or not.Since the Access Point relays every frame from/to anymobile host,it buffers the frames addressed to mobile hosts using the Power-Saving Mode.Every Beacon Interval– usually,100 ms –,the Access Point broadcasts a special frame,named Beacon (Figure3(a)).This frame containsa Trafﬁc Indication Map (TIM) that indicates PSM mobile hosts having at least one frame buffered at the AccessPoint.PSM mobile hosts are synchronized with the Access Point,and wake up to receive Beacons.If they areindicated in the TIM,they download the frames as is shown in Figure3(b).Speciﬁcally,the PSMmobile host sendsa special frame (ps-poll) to the Access Point by means of the standard DCF procedure.Upon receiving a ps-poll,the Access Point sends the ﬁrst data frame to the PSM mobile host,and receives the corresponding ack frame.Ifappropriate,the Access Point sets the More Data bit in the data frame,to announce other frames to the same PSMmobile host.To download the next frame,the mobile host sends another ps-poll.When,eventually,the mobile hosthas downloaded all the buffered frames,it switches to the sleep mode.To send a data frame,a PSM mobile host (if the case) wakes up and performs the standard DCF procedure.Speciﬁcally,the PSM mobile host sends the data frame,and receives an ack frame from the Access Point (Fig-ure3(c)).To summarize,a mobile device operating in Power-Saving Mode is required to be awake to performthree basicoperations:(i) receiving Beacon frames;(ii) downloading data frames fromthe Access Point;and (iii) sending dataframes to the Access Point.This remark is fundamental for the analytical characterization of PSM that have beenderived in [4] presented in Section5.3.3 Evaluation MethodologyIn the environment described above,one of the main inefﬁciencies in energy usage is listening during idle times.It iswell known that the trafﬁc generated by the reference applications exhibits different types of idle times [21,20,13,8].Speciﬁcally,idle times inside trafﬁc bursts (referred to as interarrival times) are typically very short,less than 1 s[21,20,13].On the other hand,idle times between consecutive bursts (referred to as User Think Times),are longerand may last up to 60 s and beyond [21].As the goal of PSMis reducing the energy consumption during idle times,we extensively analyze its behavior with respect to both interarrival times and User Think Times.6BeaconPIFSBeaconPIFSmobile hosttaggedtbsleepactiveAPttactiveBeacon Interval(a) receiving beacons            SIFSDataPS−PollACKSIFSBeaconBeaconmobile hosttaggedDCFproceduretmactsDIFSttAPactivesleepPIFSPIFSBeacon Interval(b) receiving framesData      SIFSACKtmobile hosttaggedDCFproceduretmactaDIFSsleepactivetAP(c) sending framesFigure 3:PSMoperations.The amount and duration of interarrival times is dictated by both the application-level protocols,and the char-acteristics of the network path between the mobile and the ﬁxed host.Thus,we analyze the PSMperformance withrespect to key applications and network parameters,i.e.,i) the average burst size;ii) the transport-level throughput;and iii) the MAC-level contention (i.e.,the number of users in the same Wi-Fi hotspot).We then study the PSM performance during User Think Times.We show that the energy consumption duringthese phases may dominate the energy consumption due to the whole trafﬁc pattern.Since PSMis far fromoptimalduring UTTs,we deﬁne and evaluate a Cross-Layer Energy Manager (XEM) that drastically reduces PSM energyconsumption during UTTs.Our analysis relies on both analytical and simulation results.Speciﬁcally,we extend the simulation model usedin [16] to implement the reference network scenario described above (see Section6for details).Furthermore,tobetter understand the PSMbehavior shown by simulation,we exploit an analytical model2we derived in [2,4,36],that provides closed formulas for the energy consumption in the cases where PSMis used or not.A brief presentationof this model is given in Section5.3.3.1 Performance IndicesOur analysis is mainly based on the following performance ﬁgures:• EC:the average amount of energy spent by the wireless interface to download NBRbursts from the ﬁxed tothe tagged mobile host,when PSM is not active (i.e.,in continuous active mode,CAM);• EP:the average amount of energy spent by the wireless interface to download NBRbursts from the ﬁxed tothe tagged mobile host,when PSM is active;• R(EP,EC):the ratio between the above indexes.This is a key index,since it shows the fraction of energyspent when PSM is active,with respect to the case when no energy management is used,and,thus,it showsthe PSM efﬁciency.Throughout the paper,we analyze energy consumption breakdowns for the different energy-saving policies underinvestigation.In that cases,more speciﬁc performance indexes are deﬁned.When meaningful,the index R(∙,∙) isalso applied to couples of those indexes,to show the relative advantage of the ﬁrst one with respect to the secondone.Admittedly,most of our analysis neglects the energy consumption of mobile-device components other than thewireless interface.Results discussed in the next section show that in our scenario this is a reasonable choice.2As shown in [2,4,36],analytical and simulation results fully agree.7d%%zoneSTPM, BSD, SPSMf 1+f15%LAPTOP90%f10%45%9%50%10%0PDAfPSM impact on the overall systemideal energy savingdue to wireless interfaceenergy managementonlythis−workzonePDALAPTOP(a)%idd%f=STPM idealBSD idealSPSM idealEnergy saving due towireless interface management only90%fPDALAPTOP15%75%PSM in our scenario020%100%theoreticallimit(b)Figure 4:PSMimpact on the overall system(a),and on the wireless interface only (b).4 Effects of PSMdelay on energy consumptionThanks to the results in [1,30,38] it is now well understood that the additional delay introduced by PSM to TCPtransfer times may even make PSMcounterproductive froman energy-saving standpoint.Even though much energyspent on the wireless interface can be saved by PSM,the other device components continue to drain energy duringthe additional transfer time.This cost may completely overwhelm the energy saved on the wireless interface.Forexample,Anand et al.[1] measure slowdown factors as high as 16x to 32x when using PSM.It can be noted that high additional delays arise when short TCP connections are used over short RTT paths.Indeed,[1,30,38] measure high additional delays in cases where the RTT between the mobile and ﬁxed hosts(measured when PSM is not active) is very short (few tens of milliseconds).Moreover,they focus on Web trafﬁcwithout persistent connections [30],and NFS-like trafﬁc [1]3,which actually generate very short TCP connections.Due to the TCP 3-way handshake,and to the slow-start algorithm,a newTCP connection requires several RTTs evento fetch a few KBytes.Furthermore,the work in [30] shows that PSM rounds every RTT up to the next 100ms,dueto the beaconing mechanism.This can be a very high additional delay for short RTTs (i.e.,in the order of few tensof milliseconds).Therefore,when PSM is used to download data over short-lived,non-persistent TCP connections,in case of short RTTs,the additional delay can be very high.We purposely choose a different scenario for our analysis.As mentioned in Section3,we consider persistent TCPconnections,as suggested by HTTP/1.1 [27].Furthermore,we focus on a broader RTT range (measured when PSMis not active),in the order of few hundreds of milliseconds.Even though Web proxies and caches tend to reducethe RTT,they cannot be used with any Web content (e.g.,cannot be used with dynamically generated pages).Moreover,it is still common to measure RTTs in the order of 200 ms and above while accessing popular servers overintercontinental paths (e.g.,accessing ebay.com,cnn.com,nasdaq.com,amazon.com between Europe and US).Inthis case,fetching data requires less RTTs,because the congestion window is already stable,since the connectionis persistent.Furthermore,the cost of rounding up every RTT to the next 100ms is reduced if the original RTT(measured without PSM) is already a few hundreds of milliseconds.Indeed,in our scenario the additional delaythat we have measured,averaged over all the experiments presented in the following,is just around 15% of theoriginal transfer time (corresponding to a 1.15x slowdown).To understand the impact of this slowdown on energy consumption,we follow a simple analytical approach.When PSM is not used,we assume that both the wireless interface and the rest of the system constantly drain a3Anand et al.in [1] focus on other types of trafﬁc as well.However,all the trafﬁc patterns for which PSM introduces high delays share thesame features.8ﬁxed amount of power,denoted as P(N)and P(B),respectively.Let us denote by t the download time of a burst,and by e(N)Cand e(B)Cthe energy spent in continuous active mode during t by the wireless interface and the restof the system,respectively.Let us ﬁnally denote by f the relative cost of the wireless interface with respect tothe rest of the system,i.e.f = P(N)/P(B).Thus,the energy spent in the burst download is eC= e(N)C+e(B)C=P(N)t +P(B)t = (1 +f)e(B)C.The use of PSM has two effects.On the one hand,it reduces e(N)C.Let us denote thisenergy saving by β,i.e.,e(N)P= βe(N)Cwhere e(N)Pis the energy spent on the wireless interface when PSM is used.On the other hand,PSM increases the energy of the rest of the mobile host because of the additional delay.If ddenotes this additional delay as a fraction of t,then we obtain e(B)P= (1 +d)tP(B)= (1 +d)e(B)C.It is now easy toevaluate the overall energetic advantage brought by PSM.Speciﬁcally,we deﬁne the index Δas Δ= (eC−eP)/eC,where eP= e(N)P+e(B)P.After simple manipulations we obtain Δ = 1 −fβ+1+d1+f.Figure4(a) plots Δ as a function of d for various values of f.Typically,f increases as the device form-factorshrinks;representative values for a laptop and a PDA are 1/9 and 1,respectively [1].Characterizing β is the taskof most part of this paper.However,we can here anticipate that β = 0.1 is a reasonable value to have a ﬁrst rough– yet signiﬁcant – picture.This value also matches other results in the literature [30,38].Figure4(a) clearlydifferentiates our work from [1,30,38].STPM [1],BSD [30] and SPMS [38] are mainly designed to operate incases when the additional delay is large (e.g.,the 16x slowdown measured by [1] corresponds to d = 1500%!).Indeed,in these cases Δdrops below0,stating that PSMactually produces an energy increase on the whole device.Instead,our work focuses on a region where d is limited,and PSM becomes effective,mostly for small form-factordevices.For this class of devices,PSMsaves a large portion of the energy consumption due to networking activities,without charging signiﬁcantly the other device components.Based on these results,hereafter we measure theenergy consumption of the wireless interface.Figure4(a) shows the theoretical limits achieved by an ideal policy that completely eliminates the wirelessinterface energy consumption (this policy clearly represents the asymptotical limit of any energy managementtechnique focused on the wireless interface).On a burst download lasting t seconds,the ideal policy consumeseI= e(B)C.Figure4(b) compares more thoroughly the PSM performance with eI.Speciﬁcally,it plots the indexid,which is deﬁned as the ratio between the energy saved by PSM,and the energy saved by the ideal policy,i.e.,id = (eC−eP)/(eC−eI) = 1−β−df.In our scenario,PSMachieves 75%of the ideal energy saving.It is interestingto note that the additional delay reduces the energy saving just by 15%.Furthermore,the id index can be used alsoto roughly understand the maximum expected improvement of STPM,BSD and SPMS over PSM in our scenario.STPM activates or deactivates PSM based on predictions about the future trafﬁc proﬁle.This way,it reduces theadditional delay to negligible values.In the best possible case,it spends on the wireless interface the same energyspent by PSM,without increasing the energy consumption of the rest of the device.Setting d = 0 in the id formulathus gives the maximumenergy saving of STPM.Getting analytical results for BSD and SPSMis not straightforward.However,by inspecting the results provided in [30,38] we can still derive some limit.SPSM is generally able toavoid the additional delay,and in several cases achieves the same wireless interface energy consumption of PSM.Thus,the id value for d = 0 is a good indication for the maximum SPSM performance,as well.Also BSD is ableto reduce the additional delay to negligible values.[30] shows that this is often achieved without increasing thewireless interface energy consumption with respect to PSM.We thus consider the same maximum energy savingfor BSD,as well.These optimal values are indicated in Figure4.STPM,BSD,and SPSM seem able to improve theperformance of PSMalso in our scenario.Nevertheless,we believe that PSMstill represents a valid option,becausei) it achieves signiﬁcant energy saving anyway,ii) it is already available on most of the commercial devices,and iii)it is thus a free-of-charge solution.It should also be noted that the real performance of STPM,BSD and SPSMcan belower than the values in Figure4(b).As an example,[30,38] show that,in order to eliminate the additional delay,BSD might increase the energy spent on the wireless interface with respect to PSM.On the other hand,in orderto keep the same energy saving,BSD must introduce additional delays around 14%.Similar remarks suggest that,9though being very effective when d is high,BSD,STPMand SPSMdo not performfar fromPSMin our scenario.Asa ﬁnal remark,it should be noted that the above discussion applies to the burst download phases.We postpone asimilar discussion about UTTs to Section7,to have the chance of including also XEMin the picture.The above remarks show that there is a broad range of cases in which using PSMas an energy-saving techniqueis advisable.Therefore,we now analyze in depth the PSM performance in terms of energy saving.5 Analytical modelWith reference to the network scenario and the application-level trafﬁc model depicted in Figure2(a) and Fig-ure2(b),respectively,in this section we derive a model for evaluating the average energy spent by the taggedmobile host to download NBRbursts from the ﬁxed host (any two consecutive bursts are separated by a UserThink Time).Due to space reasons,we here present the main analytical results,and we skip many detailed proofs.Interested readers can refer to [2,4,36] for all the details.To model the tagged mobile-host behavior we utilize the following approach.We replicate n times the downloadof NBRbursts,and focus on the generic i-th replica.E(i)Pand E(i)Cdenote the energy spent during the i-th replicawhen PSM is enabled and disabled,respectively.In the following,we derive closed formulas for E(i)Pand E(i)C,andshow that

ni=1E(i)Cn(1)By introducing the closed formulas for E(i)Pand E(i)Cin Expressions1,we ﬁnally obtain the closed formulas for EPand EC.As far as E(i)C,it is worth noting that,when PSM is disabled,the wireless interface of the tagged mobile host isalways active.Hence,if T(i)denotes the duration of the i-th replica (also referred to as the download interval),andPacdenotes the power drained by the tagged mobile host in the active mode,E(i)Ccan be expressed asE(i)C= T(i)∙ Pac.(2)On the other hand,when PSM is enabled,the tagged mobile host remains active just for a portion (T(i)ac) of thedownload interval5,while it is sleeping for the rest of the time (T(i)sl).Therefore,if Pslis the power drained by thetagged mobile host in the sleep mode,E(i)Pcan be expressed as follows:E(i)P= T(i)ac∙ Pac+T(i)sl∙ Psl= T(i)ac∙ (Pac−Psl) +T(i)∙ Psl.(3)Equations2and3show that both E(i)Pand E(i)Cdepend on T(i)and T(i)ac.In the following subsections we deriveT(i)and T(i)ac,respectively.5.1 Modeling the download interval (T)With reference to a generic i-th replica,T(i)may be thought of as made up of two components (see Figure2(b)):i)the total time during which bursts are downloaded (T(i)data),and ii) the total inactive time due to User Think Times(T(i)idle).Denoting by td(i)kthe time required by the tagged mobile host to download the k-th burst in the i-th replica,and by UTT(i)kthe duration of the k-th User Think Time in the i-th replica,T(i)can be written as follows:4To simplify the notation,in the following we omit indicating the range of variability of i,e.g.nE(i)Poi,...,nis referred to asnE(i)Po5T(i)acalso includes the transition times from the sleep to the active mode.10T(i)= T(i)data+T(i)idle=N(i)BR

k=1td(i)k+N(i)BR

k=1UTT(i)k.(4)It can be shown that

T(i)data

and

T(i)idle

are composed by identically distributed randomvariables.Furthermore,for each couple i,k,N(i)BRand td(i)k,as well as N(i)BRand UTT(i)k,are mutually independent.It is also worth point-ing out that we assume that TCP always works in the steady state (i.e.,we do not consider slow-start phases),whichis a common assumption in the literature [35],and very reasonable in the case of persistent connections.Further-more,to simplify the analysis,we approximate the steady-state TCP throughput with a constant value6,hereafterreferred to as γTCP.Therefore,if E[d] denotes the average burst size,after simple manipulation the average valueof the download interval can be expressed as:E[T] = E[NBR] ∙

E[d]γTCP+E[UTT]

.(5)5.2 Modeling the time spent in the active mode (Tac)Since we are assuming a TCP/IP architecture,the trafﬁc on the WLAN related to the tagged mobile host includes:i) TCP segments7coming from the ﬁxed server;ii) TCP acks sent by the tagged mobile host to the ﬁxed server;and iii) Beacon frames periodically broadcast by the Access Point.Thus,T(i)acis the time spent in the active modeby the tagged mobile host to handle these trafﬁc components.Before proceeding on,we need to introduce someassumptions and emphasize some properties related to our model.Property 1.In the following,we assume that Beacon frames are safely transmitted,i.e.,they do not collide withtransmissions fromany other mobile host.In other words,the MAC protocol guarantees that the shared mediumisidle at the beginning of each Beacon Interval,and no transmissions are attempted until the Beacon frame is received(see Figure3(a)).This assumption is aligned with the most up-to-date proposals within the 802.11 working groups[28].Property 2.Let us deﬁne a sequence of frames as a set of frames exchanged between the mobile host and the AccessPoint,where each frame is spaced fromthe previous one by a SIFS interval.According to the 802.11 DCF deﬁnition[29],in our WLAN environment only the ﬁrst frame of a sequence can undergo collision.In other words,either theﬁrst frame of a sequence collides,or the whole sequence is safe.Property 3.Each TCP segment sent by the ﬁxed host to the tagged mobile host is encapsulated into a distinct IPpacket.Thus,if we assume that both IP- and 802.11 MAC-level fragmentation are disabled,the tagged mobile hostdownloads each TCP segment from the Access Point inside a distinct data frame.Since we also assume that theRTS/CTS mechanism is disabled,downloads occur by exchanging a sequence of frames including a ps-poll,data,and ack frame (between the tagged mobile host and the Access Point),as shown in Figure3(b).Similarly,each TCPack is uploaded to the Access Point inside a distinct data frame,i.e.,by exchanging a sequence of frames composedby a data and an ack frame (see Figure3(c)).Property 4.Let i) s(i)jbe the time required by the tagged mobile host to download the generic j-th TCP segmentduring the i-th replica,starting from the point in time when the tagged mobile host starts the DCF procedure tosend the related ps-poll frame;ii) a(i)rbe the interval required by the tagged mobile host to upload the r-th TCP ackduring the i-th replica,starting from the point in time where the tagged mobile host starts the DCF procedure tosend the related data frame;and iii) b(i)lbe the time required by the tagged mobile host to receive the l-th Beaconframe during the i-th replica,starting fromthe beginning of the related Beacon Interval.Then,for any triple j,r,l,6The validation of the analytical model carried out in [2,4,36] shows that these assumptions do not compromise the accuracy of theanalytical results.7For the sake of simplicity,we indicate TCP segments containing application data as TCP segments,while TCP acks denote TCP segmentscontaining just acknowledgments.11it can be shown that the time intervals s(i)j,a(i)rand b(i)ldo not overlap.Based on the above properties,T(i)accan be regarded as the sum of times required to i) receive all the Beaconframes,ii) download all the TCP segments,and iii) upload all the TCP acks,within a download interval i.e.T(i)ac=N(i)seg

j=1s(i)j+N(i)ack

r=1a(i)r+N(i)b

l=1b(i)l.(6)In (6),N(i)seg,N(i)ackand N(i)bare the number of TCP segments,TCP acks,and Beacon frames exchanged (betweenthe mobile host and the Access Point) during the i-th replica,respectively.In our model:i) the number of TCP seg-ments downloaded is equal to the number of TCP acks uploaded (i.e.,N(i)seg= N(i)ack);and ii) b(i)lcan be reasonablyapproximated with a constant value,throughout referred to as b.Finally,by analyzing the properties of the randomvariables N(i)seg,N(i)ack,s(i)jand a(i)r,it can be shown that the average value of Taccan be expressed in a very intuitiveway:E[Tac] = E[Nseg] ∙ (E[s] +E[a]) +E[Nb] ∙ b.(7)Deriving closed formulas for E[s] and E[a] would require a detailed analysis of the 802.11 DCF function.Thecomplete model accounting for all the DCF details (retransmissions,contentions,etc.) is derived in [2,4,36],andis here omitted for the sake of space.The main issue to be highlighted here is that both E[s] and E[a] includetwo components,i.e.the average MAC delay and the average sequence time (see Figure3(b,c)).The average MACdelay is deﬁned as the interval between the time when the DCF procedure is invoked to transmit a frame,and thetime of the successful transmission (possibly after a number of unsuccessful attempts).Intuitively,this componentis statistically equivalent for both TCP segments and TCP acks,and corresponds to the time spent by the taggedmobile host in the DCF procedure to send the ps-poll frame and the data frame containing the TCP ack,respectively(see Figure3(b,c)).The average sequence time is deﬁned as the interval between the time when the transmissionof the ﬁrst frame in a sequence starts,and the time when the reception of the last frame in that sequence ends.Clearly,the average sequence time depends on the frames in the sequence,and is thus different for TCP segmentsand TCP acks (see Figure3(b,c)).The last step to derive a closed formula for E[Tac] is evaluating E[Nseg] and E[Nb].It can be shown that theaverage number of TCP segments exchanged during a download interval (E[Nseg]) is equal to the average size ofall bursts downloaded in that replica,divided by the MaximumSegment Size (MSS) of the TCP connection,i.e.,E[Nseg] =E[NBR] ∙ E[d]MSS.(8)In addition,the average number of Beacon frames received by the tagged mobile host during a replica (E[Nb]) isthe ratio between the average download-interval duration,E[T],and the duration of a Beacon Interval,BI:E[Nb] =E[T]BI.(9)By substituting Equations8and9into Equation7we obtain the following closed formula for E[Tac]:E[Tac] =E[NBR] ∙ E[d]MSS∙ (E[s] +E[a]) +E[T]BI∙ b.(10)Finally,by introducing Equations2and3into Expression1,after simple algebraic manipulations,ECand EPcanbe expressed as follows:

EC= E[T] ∙ PacEP= E[Tac] ∙ (Pac−Psl) +E[T] ∙ Psl,(11)where E[T] and E[Tac] are given by Equations5and10,respectively.120 10 20 30 40 500200400600800Energy in CAMactive mobile hosts (M)Energy(J)analysissimulation(a) CAM0 10 20 30 40 500200400600800Energy in PSMactive mobile hosts (M)Energy (J)analysissimulation(b) PSMFigure 5:Example of validation plots.6 Evaluating the 802.11 Power Saving ModeAs mentioned in Section3,the performance analysis of PSM is carried out by using both simulation and the an-alytical model derived in the previous section.Speciﬁcally,analytical results are used to provide better insightsin the PSM behavior highlighted by simulation.As an example of the agreement between the analytical and thesimulation model,Figure5shows two of the validation plots for ECand EPpresented in [36].According to the idle-time classiﬁcation presented in Section3,we analyze PSM performance by consideringseparately its behavior during interarrival times – i.e.,during bursts (Section6.2),and during User Think Times(Section6.3).6.1 Simulation EnvironmentOur simulator extends the model used in [16],and implements the reference environment described in Section3.It simulates a full-compliant 802.11 hotspot (populated by a variable number of background mobile hosts),andfull-compliant TCP-Reno between the mobile and the ﬁxed host.Please note that the simulation model implementsall the features of both 802.11 and TCP.To allowfor signiﬁcant values of burst sizes and User Think Times we makereference to the Web trafﬁc.Therefore,each burst corresponds to the download of a Web page.In particular,weconsider the statistical models of the Web trafﬁc presented in the well-known works by Crovella et al.[13,20].A typical simulation run proceeds as follows (Table1summarizes the default values for the main simulationparameters).The tagged mobile host downloads NBRbursts from the ﬁxed server (recall that two consecutivebursts are spaced by a User Think Time).The average Web-page size and User Think Time duration (i.e.,E[d]and E[UTT] in Table1) are derived from [13,20].However,we tested the system over a wide range of burstand UTT values,making the analysis valid for the general trafﬁc model presented in Section3,and not only forthe Web case.To mimic a realistic TCP connection between the mobile host and the ﬁxed server,Internet RoundTrip Times (as would be measured without PSM) are sampled froman exponential distribution (the default averagevalue – RTT – is reported in Table1).To simulate packet losses at Internet routers,TCP segments are randomlydropped with probability ptcpl.Note that ptcpljust accounts for losses in the wired network,due to routers’ bufferoverﬂow.The additional packet loss due to the WLAN depends on the MAC protocol behavior,and is thus not asimulation parameter (it can actually be derived by simulation).Finally,energy parameters are as follows.Thepower consumptions in the sleep and active modes (i.e.,Psland Pac) are the same as those used in [30].These13Parameter Value UnitParameter Value UnitNBR100 -MSS 1460 BE[d] 20.19 KBPsl50 mWE[UTT] 3.25 sPac750 mWRTT 150 mstsa1 msptcpl1% -BI 100 msTable 1:Default simulation parametersare quite similar to values used in other well-known analyses [23],and comparable to recent datasheets [18].tsadenotes the time required by the wireless interface to switch from the sleep to the active mode.Note thatthis parameter allows us to also include the cost of switching between the wireless interface operating modesaccording to PSM.Its default value is derived from the measurements in [30].Speciﬁcally,[30] measured that,while operating in PSMmode,the wireless interface spends about 2 ms to switch fromthe sleep to the active modeand to receive a Beacon Frame.Based on the 802.11 standard [29] it is easy to show that the time required toreceive a Beacon Frame is about 1 ms.Therefore,we assume 1 ms as the time required by the hardware to switchfromthe sleep to the active mode.We do not consider the impact of different tsavalues on PSM,because we focusmore on networking and application-level parameters.Note that the switching time between operating modes (andthus the related costs) could be reduced signiﬁcantly by improved hardware design (similarly to what is happeningfor channel switching in the mesh networks domain).Finally,the value of the Beacon Interval (BI) is the onesuggested by the 802.11 standard [29].To increase the results’ reliability,each simulation experiment is replicated10 times.Conﬁdence intervals reported throughout the paper have 95% conﬁdence level.6.2 PSMperformance during burstsBursts and interarrival times are determined by both application and networking protocols.In our scenario,wheredata mainly ﬂowfromthe ﬁxed server to the tagged mobile host,the application dictates the burst sizes8,while theTCP protocol is the main responsible for interarrival times.Thus,we now focus on the impact of two parameters,i.e.i) the average burst size (Section6.2.1),and ii) the TCP-connection throughput (Section6.2.2).In both cases,we assume a single mobile host in the hotspot (i.e,M = 0).Section6.2.3extends the analysis by consideringseveral mobile hosts in the same hotspot (i.e.,M > 0).6.2.1 Impact of the burst sizeAs mentioned above,in our model each burst corresponds to the entire download of a Web page.There is a wideconsensus about the type of distribution for modeling page sizes (see,for example,[13,20,8]).On the other hand,the average value of this distribution can be highly variable,and can range from20 KB up to few MB [13,20,21].Based on these remarks,in our simulation model the burst-size distribution is deﬁned by the randomvariable a ∙ S,where:i) a is a (integer) scaling factor,and ii) S is the randomvariable deﬁning the page size distribution derivedin [13,20].The average burst size can thus be scaled (by varying a) without modifying the distribution’s coefﬁcientof variation.This allows us to evaluate PSMunder realistic trafﬁc loads.Speciﬁcally,we report a set of experimentswhere a varies between 1 and 100,while the average of S (denoted by µ) is set to 20 KB [13,20].This resultsin an average burst size ranging from 20 KB to about 2 MB.We believe this range also represents the trafﬁc whentechniques such as loss-less compression or AJAX [25] are used,i.e.techniques that signiﬁcantly reduce the amount8E.g.,in the Web case the burst sizes are determined by the content the user is downloading.140 20 40 60 80 1000100020003000400050006000scaling factor (a)Energy (J)CAMPSM(a) Energy plots0 20 40 60 80 1000.000.050.100.150.200.250.30scaling factor (a)R(Ep,Ec)(b) R(EP,EC)Figure 6:PSMperformance as function of the average burst size (a ∙ µ).of data exchanged over the network per user request.More in general,we believe that this range represents all ourreference applications.As in this set of experiments we intend to investigate the PSMperformance during bursts,User Think Times arealways set to 0.Since the TCP-connection evolution depends on i) the average Round Trip Time (RTT) and ii) thesegment-loss probability (ptcpl) [35],and both parameters can be reasonably assumed to be independent of the UserThink Time duration,setting UTT to 0 is justiﬁed.Figure6(a) plots EP(bottomcurve) and EC(top curve) for different average burst sizes.The most interestingfeature is that energy increases linearly in both cases.This behavior can be explained by means of Equation11.Since we assume E[UTT] = 0 and E[d] = a ∙ µ,ECbecomes:EC= Pac∙E[NBR] ∙ a ∙ µγTCP= a ∙ µ ∙ KC,where KC Pac∙E[NBR]γTCP.(12)By following a similar line of reasoning,EPcan be expressed as follows:EP= a ∙ µ ∙ KP,(13)where KPincludes terms that are independent of both a and µ.Deriving the closed formula of KPrequires somemanipulation.It can be expressed as K1Psl+K3(Pac−Psl) where K3= K2+K1∙ b/BI,K2= (E[s] +E[a]) ∙E[NBR]/MSS,and K1= E[NBR]/γTCP(see [2,36]).The linear increase of ECand EPwith the average burst size has also an intuitive explanation.ECis propor-tional to the the average download interval (E[T],see Equation11).Assuming E[UTT] = 0,the average downloadinterval coincides with the average time spent downloading the bursts from the ﬁxed host,i.e.,E[Tdata].Further-more,since the TCP throughput is assumed to be constant,E[Tdata] is proportional to the average burst size (seeEquation5).In addition,EPis a linearly increasing function of i) the average download interval (E[T]),and ii) the averagetime during which the tagged mobile host remains in the active mode (i.e.,E[Tac],see Equation11).Based onthe above remarks,E[T] is proportional to the average burst size.Now,we show that the same property holds forE[Tac],as well.E[Tac] includes two components,i.e.,the time spent – within a download interval – to receive(transmit) TCP segments (TCP acks),and to receive Beacon frames from the Access Point (see Equation10).Theaverage total time required to receive (transmit) TCP segments (TCP acks) is proportional to the number of TCPsegments (TCP acks) managed during the download interval,and,hence,to the burst size (Equation8and10).The150.001 0.005 0.020 0.050 0.200 0.50001000300050007000TCP-segment loss probabilityEnergy (J)CAMPSM0.001 0.005 0.020 0.100 0.500050100150200250300TCP-segment loss probabilityEnergy (J)(a) Energy plots0.001 0.005 0.020 0.050 0.200 0.5000102030405060TCP-segment loss probabilityEnergy (normalized)CAMPSMoff scale (150)0.001 0.005 0.020 0.100 0.50002468TCP-segment loss probabilityEnergy (normalized)(b) Multiplicative factorsFigure 7:PSMperformance as function of the TCP segment-loss probability (ptcpl).total time required to receive Beacon frames is proportional to the number of Beacon Intervals within the downloadinterval,thus to the download interval,and thus to the burst size.The results in Figure6(a) highlight an important property of PSM,which is better emphasized in Figure6(b).Figure6(b) shows the R(EP,EC) index9as a function of the a parameter.It clearly shows that R(EP,EC) is almostindependent of the average burst size.This is because EPand ECare both proportional to the burst size,and theirratio depends on the parameters that deﬁne KPand KC.In our experiments,this value is around 0.16,resulting inan energy saving of approximately 84%.Based on Figure6(b) we can claim that the energy saved by PSM does notsigniﬁcantly depend on the average burst size.Therefore,in the following experiments,unless otherwise stated,weassume a = 1.6.2.2 Impact of the Internet throughputIn this Section we investigate the impact on the PSMperformance of the Internet throughput.The results presentedin [35] show that the segment-loss probability (ptcpl) and the average Round Trip Time (RTT) are the main pa-rameters that impact on the throughput of a TCP connection (γTCP).Speciﬁcally,γTCPis a decreasing function ofboth.Thus,we ran a set of simulation experiments to investigate the PSM behavior with respect to ptcpland RTT,respectively.According to [35],the lower and upper values of ptcplare set to 0.001 and 0.5.E[UTT] is set to 0,as above,while the rest of the simulation parameters are as in Table1.Figure7(a) plots EP(bottom curve) and EC(topcurve) as functions of ptcpl.As expected,both EPand ECincrease with ptcpl.It is well known that increasing ptcpltremendously reduces the TCP throughput.The average duration of the download interval (E[T]) increases,andthis results in an increase of both ECand EP.The additional download time mainly consists of longer idle timesbetween burst segments.When PSM is not active,the additional time is spent completely in the active mode.When PSM is active,the additional time is only partly spent in the active mode (due to Beaconing),and mostlyspent in the sleep mode.Hence,PSM is able to greatly reduce the negative effect of low throughput on the energyconsumption.To quantify this behavior,let us focus on Figure7(b) that shows the energy consumed at a givensegment loss probability ptcpl,normalized to the energy consumed at ptcpl= 0.001.In a sense,Figure7(b) shows,for each ptcplvalue,the “energy multiplicative factor” with respect to the energy consumption at ptcpl= 0.001.For9Recall that this index represents the fraction of energy spent when PSM is active,with respect to the energy spent when PSM is not active.Hence,it shows the energy saved by PSM.160 200 400 600 800 1000050100150200250300350RTT (ms)Energy (J)CAMPSM(a) Energy plots0 200 400 600 800 1000024681012RTT (ms)Energy (normalized)CAMPSM(b) Multiplicative factorsFigure 8:PSMperformance as function of the Round Trip Time (RTT).example,when ptcplis equal to 0.1,the multiplicative factor for ECand EPis around 7x and 3x,respectively.Themultiplicative factor when PSM is active is always lower than when it is not.Furthermore,the more ptcplincreases,the more the difference between the two curves increases.A similar result is also obtained when analyzing the dependence of the energy consumption on RTT (Fig-ure8(a,b)).Though the absolute values are different fromthose in Figures7(a) and7(b),the qualitative behavioris the same.Hence,we can conclude that the energy consumption is negatively affected by low TCP throughput,either PSMis active or not.However,PSMgreatly helps in mitigating this effect.6.2.3 Impact of the WLAN contentionSo far,the analysis has been carried out under the assumption of a single mobile host within the Wi-Fi hotspot,i.e.,M has been assumed to be equal to 0.Now,we evaluate the impact of MAC-level contention on the mobile-hostenergy consumption (i.e.,M > 0).To this end,we ﬁrstly highlight the limitations of PSM when a standard TCParchitecture is used.Then,we investigate up to what extent an Indirect-TCP architecture [11] can alleviate theseproblems.The simulation parameters are as shown in Table1,apart fromE[UTT] which is set to 0,as above.802.11 PSMin a standard TCP architectureFigure9(a) plots EPand ECAs expected,both EPand ECincrease when the contention in the WLAN increases.This behavior stems fromtwo causes:on one hand,MAC-level contention reduces the TCP throughput;on the otherhand,it increases the MAC delay.The impact of the WLAN contention on the TCP throughput clearly appears from Figures9(b,c,d).The frameloss probability on the WLAN increases with M (Figure9(b)).This results in increased number of timeouts at theTCP sender (Figure9(c)),and,ultimately,to a severe degradation of the TCP throughput (Figure9(d)).In addition,it is well known that increasing the MAC-level contention increases the MAC delay [15].When theMAC delay increases,the time required for receiving a TCP segment (i.e.,E[s] in Equation10),or sending a TCPack (i.e.,E[a] in Equation10),increases accordingly.So,the time interval during which the tagged mobile host isactive (E[Tac]),and hence EP,increases with M (see Equations10and11).Clearly,similar remarks apply to ECas well.Based on these remarks,two factors are responsible for the increased energy consumption when M increases,i.e.,i) the reduced TCP throughput (due to an increase in the frame loss probability);and ii) the increased MAC170 10 20 30 40 500100200300400500600700active stations (M)Energy (J)CAMPSM(a) Energy expenditure0 10 20 30 40 500.000.050.100.150.20active mobile hosts (M)WLAN frame loss probabilityPSM(b) WLAN frame loss probability0 10 20 30 40 50010203040506070active mobile hosts (M)number of timeoutsPSM(c) number of TCP timeouts0 10 20 30 40 50050100150200250active mobile hosts (M)TCP throughput (Kbps)PSM(d) TCP throughputFigure 9:802.11 PSMperformance in a standard TCP architecture.delay.In the following of this section,we decouple the effects of these two factors,to understand the real impact ofeach one.Speciﬁcally,we show that using an Indirect-TCP architecture [11] eliminates factor i),and explains thediscrepancy between the EPand ECcurves in Figure9(a).802.11 PSMin an Indirect TCP architectureIn an Indirect-TCP architecture [11],the transport connection between the mobile host and the ﬁxed host is splitin two distinct parts at the boundary between the wireless and the wired networks (i.e.,at the Access Point).Anagent (the Indirect-TCP Daemon) relays the data between the two parts of the connection granting transparency tothe application level.It has been proved [12] that this architecture shields the TCP sender at the ﬁxed host fromthe losses on the wireless link,thus increasing the throughput with respect to the legacy TCP architecture.We show that this “shielding property” can be exploited to eliminate the energy wastage related to the transportmobilehostapplicationSTPIP802.11fixedhostapplicationTCPIPMACAccessPointSTPIP802.11TCPIPMACI−TCPDaemonFigure 10:Indirect-TCP architecture180 10 20 30 40 500100200300400500600700active stations (M)Energy (J)CAMPSM(a) Energy expenditure0 10 20 30 40 500.00.20.40.60.81.0active stations (M)Idleness indexPSM(b) Idleness index0 10 20 30 40 500.000.050.100.150.20active mobile hosts (M)WLAN frame loss probabilityPSM(c) WLAN frame loss probability0 10 20 30 40 50010203040506070active mobile hosts (M)number of timeoutsPSM(d) number of TCP timeouts0 10 20 30 40 50050100150200250active mobile hosts (M)TCP throghput (Kbps)PSM(e) TCP throughputFigure 11:802.11 PSM performance in an Indirect-TCP architecture.protocol (i.e.,cause i) above).To this end,we run simulations by replacing the standard TCP architecture with thearchitecture shown in Figure10.This is similar to the original Indirect TCP,except for the transport protocol usedover the WLAN.Speciﬁcally,we use the Simpliﬁed Transport Protocol (STP),which is essentially a Stop-and-Waittransport protocol,optimized for the one-hop wireless environment [6,3].Figures11(c,d,e) show that the Indirect-TCP architecture actually shields the TCP sender at the ﬁxed host fromframe losses in the WLAN (note that the TCP throughput is measured at the ﬁxed host).Speciﬁcally,even thoughthe WLAN frame loss probability increases just as in the legacy TCP architecture (compare Figures11(c) and9(b)),the number of timeouts registered at the TCP sender (Figure11(d)) and the throughput experienced by the TCPconnection over the wired network (Figure11(e)) are independent of that.Hence,the effect of the reduced TCPthroughput on the energy consumption,registered in the previous set of experiments,disappears.Only the MAC-delay increase (cause ii) above) is thus responsible for the additional energy consumption.It should be noted thatPSMis not able to face this problem,as it appears fromFigures11(a,b).Figure11(b) shows the Idleness index as afunction of M.The Idleness index is deﬁned as the fraction of time (within bursts) during which the tagged mobilehost is idle because there are no frames buffered for it at the Access Point.When the WLAN contention is high(M = 50) the transport-level throughput on the WLAN is lower than the TCP-throughput on the wired part of theconnection.Hence,the TCP sender pumps data towards the Access Point at a higher rate than the tagged mobilehost could fetch fromthe Access Point.So,the tagged mobile host is never idle,and the PSM can never switch thewireless interface to the sleep mode.In conclusion,for high contention levels,either enabling the PSMor not leadsto similar results (Figure11(a)).Based on these observations,we can conclude that the effect of the MAC delay onthe energy consumption can be contrasted only by reducing the MAC delay itself through MAC-level modiﬁcations19(e.g.,as proposed in In [14]).To summarize,the results presented so far show that in our reference scenario PSM works very well duringbursts,i.e.,it manages interarrival times very effectively.Speciﬁcally,we have shown that:i) the energy savingachieved by PSM is almost independent of the size of bursts that are downloaded,and,for typical values of themain Internet parameters,it can be as high as 84%;and ii) PSM is able to limit the energy consumption when thethroughput offered by the TCP connection drops.6.3 Is PSMeffective with any class of idle times?Since PSMjust exploits the sleep mode of the wireless interface,one could argue that it could be improved by usingthe off mode instead.However,this would cost additional delay and energy consumption upon re-activation.Whilethe transition time fromthe sleep to the active mode (tsa) is in the order of 1 ms,the transition time fromthe off tothe active mode (throughout referred to as toa) is quite greater.The work in [1] measured a transition time around400 ms,while [43] measured a transition time around 100 ms10,which is the value we use hereafter.As highlightedin Section6.1,we choose not to focus on the impact of different switching times on energy consumption.Intuitively,the sleep mode should be more appealing for “short” idle times,while for “long” idle times the best choice shouldbe switching the wireless interface off.In this section we corroborate this claim by means of the analytical modelintroduced in Section5.This suggests some directions to improve the standard PSM.Let us focus on an idle time of a given length (say,ti),and let us deﬁne the behavior of two ideal energymanagers,just exploiting the sleep and the off mode,respectively.In the ideal case,these energy managers knowa-priori the length of the idle time.The energy manager that uses the sleep mode keeps the wireless interfacesleeping up to tsaseconds before the idle-time endpoint.If ES(ti) denotes the energy spent by this energy managerduring ti,the following equation holds:ES(ti) = (ti−tsa) ∙ Psl+tsa∙ Pac= ti∙ Psl+(Pac−Psl) ∙ tsa.(14)On the other hand,the ideal energy manager that uses the off mode lets the wireless interface in the active modeif tiis less than toa.Otherwise,it switches it off,and reactivates it toaseconds before the idle-time endpoint.IfEO(ti) denotes the energy spent in this case,the following equation holds:EO(ti) =

ti∙ Pacif ti≤ toatoa∙ Pacotherwise.(15)Figure12(a) plots Equations14(“ideal sleep” curve) and15(“ideal off” curve) as functions of ti.It conﬁrms thatfor “short” idle times the best policy consists in putting the wireless interface in the sleep mode,while for “long”idle times the off-based policy exhibits the best performance.Letˆtidenote the crossing point between ES(ti) andEO(ti).Then,the optimal (ideal) policy is a mixed policy that uses the sleep mode for idle times lower thanˆti,and the off mode for idle times greater thanˆti.This analysis also suggests that mixed policies using both the sleepand the off modes should be deﬁned when “short” and “long” idle times coexist,as in the case of our referenceapplications.Let us now analyze the energy spent by PSM during ti.Since the station is active just to receive Beacons,theaverage energy spent by PSMduring ti(EP(ti)) is:EP(ti) =

ti−tiBI∙ b

∙ Psl+tiBI∙ b ∙ Pac= ti∙

Psl+(Pac−Psl) ∙bBI

.(16)Equation16is plotted in Figure12(a),with label “PSM”.This plot conﬁrms that PSM is effective with respect tointerarrival times,i.e.,for idle times below 1 s.The additional energy expenditure achieved by PSM with respect10Actually,100 ms is the time measured for a complete cycle active-off-active.Since in our analysis the breakdown between the active-off andoff-active times is not important,we assume 100 ms as the off-active transition time.200 500 1000 1500 2000 2500 30000.000.050.100.15idle time duration (ms)Energy (J)PSMideal sleepideal offtimeout-based off(a) off- and sleep-based strategies0 10 20 30 40 50 6005101520UTT duration (s)R(Eutt,Ebr)a=1a=10a=100off scale (41)(b) Relative cost of UTTs and bursts in PSMFigure 12:Evaluation of PSMduring User Think Times.to the “ideal-sleep” policy is always below 20%.Thus,in this region,PSMis a close approximation of the best,ideal,policy.Figure12(a) also shows that the PSM discrepancy with off-based policies increases as idle times become longerand longer.The “ideal-off” policy cannot be implemented in practice.However,let us consider a very simpletimeout-based policy that lets the mobile host active for the ﬁrst toaseconds of an idle time,and then switches itoff11.The energy spent by this policy is plotted in Figure12(a) for comparison (“timeout-based off” label).Thispolicy is known to be 2-competitive,i.e.,it never consumes more than twice the energy spent by the ideal off-basedpolicy [26].Though this policy can be signiﬁcantly improved [26,3],it performs better than PSM for idle timeslonger than 2.5 s,and even better than the “ideal-sleep” policy for idle times longer than 3 s.Therefore,designinga mixed policy that exploits the off mode during long idle times and PSM during short idle times is an interestingdirection to pursue.Before analyzing in detail how such improvements can be implemented in a feasible way,let us further investi-gate how much energy is spent by PSM during User Think Times,with respect to the energy spent during bursts.This indicates if it is actually worth designing a system that reduces the energy spent during User Think Times.Letus deﬁne EBRas the average energy spent by PSM to download a single burst,and EUTTas the average energyspent by PSM during a User Think Time12.In Figure12(b) the index R(EUTT,EBR) is plotted for increasing UserThink Times.Three different plots are drawn for three different average burst sizes,i.e.,a = 1,a = 10,and a = 100.Figure12(b) shows that the energy spent during User Think Times is not negligible with respect to the energy spentduring bursts,for any average burst size.Speciﬁcally,for small bursts (i.e.,a = 1),R(EUTT,EBR) is around 20 forUTTs equal to 30 s,and raises up to about 40 for UTTs equal to 60 s (not shown in the plot).Even for large bursts(i.e.,a = 100),the energy spent during the User Think Times is about 25% for UTTs equal to 30 s,and about 50%for UTTs equal to 60 s.This is a strong motivation to look for possible improvements of PSM in the region of longidle times.21tswitchnetworktrafficobservePSMdetectionunitOffXEMFigure 13:Cross-Layer Energy Manager:a conceptual scheme7 Enhancing PSM:a Cross-Layer ApproachThe Cross-Layer Energy Manager (XEM) implements a mixed policy.A conceptual scheme of XEM is shown in Fig-ure13.XEM observes the trafﬁc generated by the tagged mobile host,and switches the wireless interface betweenPSM and off mode accordingly.Thus,XEM includes a detection unit that implements two detection algorithms fordetecting the beginning of bursts and User Think Times,respectively.Unlike PSM (and many of its modiﬁcationsreferred in Section2) XEM does not work exclusively at the MAC level.Instead,it exploits information related todifferent layers in the protocol stack,and thus leverages the powerful cross-layer approach [19].In the following of this section we deﬁne some possible detection algorithms,and evaluate the correspondingXEM implementations.Although very simple,these implementations are very effective solutions.Furthermore,it should be noted that,thanks to its ﬂexible design,XEM is able to accommodate also different (possibly moresophisticated and even more effective) detection algorithms and energy-saving policies.For example,during burstsit would be possible to use BSD,STPMor SPSM,instead of PSM.7.1 Burst detectionIn a Wi-Fi hotspot,detecting the beginning of a burst is usually not a big deal.The main applications that aresuitable to be deployed in Wi-Fi hotspots (e.g.,Web,mail,ﬁle download) follow a client/server paradigm,themobile host acting as the client.Thus,bursts represent data that are downloaded after the mobile host has senta request to the ﬁxed host.In other words,it is reasonable to assume that the ﬁrst segment of a burst is sent bythe mobile host.Under this assumption,the beginning of a burst can be easily detected at the mobile host,andidentiﬁed by the request sent by the client application (typically after a User Think Time).Therefore,XEM simplylets the mobile host in the off mode during User Think Times,and switches it to the standard PSMas soon as a newapplication-level request is detected.Section7.4discusses how to extend XEMto more general scenarios.7.2 User Think Time DetectionUser Think Time detection could exploit knowledge about the application(s) behavior.For example,[6] presentstwo different energy-management policies designed to support Web-based applications13and implemented at themiddleware layer.They both rely on an agent at the mobile host that spoofs the Web trafﬁc generated by the user.For each Web page,this agent is aware of the set of ﬁles composing the page itself.Once all of these ﬁles have beendownloaded,a User Think Time is assumed to start.This allows detecting User Think Times as soon as they start.11This policy is feasible if one supposes that the mobile host is immediately aware of the availability of the ﬁrst segment next to the idle time.We discuss this point in Section7.12EBRcan be easily computed from the analytical results provided in Section5,while EUTTis equal to EP(E[UTT]).13The reference environment is similar to the one considered in this paper.221:while true do2:netinterfacemode = PSM3:collect the list of ﬁles composing the Web page4:repeat5:spoof the application-level trafﬁc6:until the whole Web page is at the mobile host7:netinterfacemode = off8:wait(Web-page request fromthe application)9:end while1:while true do2:netinterfacemode = PSM3:isUTT =false4:repeat5:wait(beginning of idle time)6:itend =false7:repeat8:t=update the idle-time duration9:if t ≥ tTOthen10:isUTT = true11:else if a new segment is either sent or received then12:itend =true13:end if14:until itend==true or isUTT==true15:until isUTT==true16:netinterfacemode = off17:wait(packet fromthe application)18:end whileFigure 14:Cross-Layer Energy Managers:A-XEM(left) and T-XEM(right)A ﬁrst way for detecting User Think Times in XEM is inspired by this approach.This version of XEM includesa middleware agent that is aware of the speciﬁc application running on the mobile host (e.g.,Web browsing).As this implementation of the Cross-Layer Energy Manager depends on the speciﬁc application it is designed for,it is hereafter referred to as the Application-dependent Cross-Layer Energy Manager (A-XEM).The pseudo-codespeciﬁcation of this Energy Manager is shown in Figure14(left) (Web browsing is used as the reference application).Let us focus on line 2,and assume that a burst just started.According to the general XEM scheme depicted inFigure13,A-XEM relies upon the standard PSM during bursts (lines 2-6),and switches the wireless interface offduring User Think Times (lines 7-8).The completion of a Web-page download triggers the start of a User ThinkTime (line 6),while a new request from the user indicates that a new burst is starting (line 8).Please note that,apart fromthe PSMfunctionalities already included in the Access Point,A-XEMcan be entirely implemented at themobile host.A-XEM uses an off-based policy to manage User Think Times,which may be suboptimal for very short UTTs.Asit is shown in Section7.3,the penalty paid for this – in terms of energy consumption – is very limited.Moreover,additional mechanisms should be included to improve A-XEM performance during short User Think Times.In thispaper we decide not to explore this direction in order to keep the A-XEMdeﬁnition simple.A-XEM is strictly tied with the application it is designed for.Hence,a customized energy manager is needed foreach network application.Furthermore,a coordination between different energy managers is needed in presenceof concurrent applications.These drawbacks can be overcome,at the cost of a little performance degradation,byimplementing an application-independent Cross-Layer Energy Manager.In the following we deﬁne a Cross-LayerEnergy Manager that relies upon a timeout-based policy to detect User Think Times.Hence,this energy manageris referred to as the Timeout-based Cross-Layer Energy Manager (T-XEM).In [3] it is shown that,in our (TCP)environment,interarrival times can be thought of as time intervals between consecutive TCP segments.Due to theTCP behavior,new TCP segments are expected (at worst) one RTT after a TCP ack has been sent by the mobilehost.If no TCP segments have arrived after one RTT,it is reasonable to assume that a UTT has started.Thus,T-XEM derives,on-line,a statistical characterization of the RTT between the mobile and the ﬁxed host.Based onthis characterization,a timeout value (denoted by tTO) is chosen,in such a way that idle times longer than tTOare,very likely,User Think Times.In other words,if at some point in time an idle time is detected,and tiis the timeelapsed fromits beginning,the equation p (tiis a UTT|ti≥ tTO) = 1 is assumed to hold.The pseudo-code speciﬁcation of T-XEM is detailed in Figure14(right).As in the case of A-XEM,T-XEM isimplemented at the mobile host (apart fromthe PSM functionalities already implemented at the Access Point).Letus focus on line 2,and assume that a burst just started.T-XEMswitches the mobile-host wireless interface to PSM,230 10 20 30 40 50 600.00.51.01.5UTT duration (s)Energy (J)RTT: 0.1, 0.2, 0.5, 1 secPSMA-XEMT-XEM0 1 2 3 4 5 60.000.050.100.150.200.25UTT duration (s)Energy (J)PSMT−XEM, RTT=1T−XEM, RTT=0.5T−XEM, RTT=0.2A−XEMT−XEM, RTT=0.1(a) Energy consumption just during UTTs0 10 20 30 40 50 600.00.20.40.60.81.01.2UTT duration (s)R(Eax,Ep), R(Etx,Ep)RTTRTTR(Eax,Ep), a=1R(Etx,Ep), a=1R(Eax,Ep), a=100R(Etx,Ep), a=100a=1a=100(b) Energy consumption during a single cycleFigure 15:Evaluation of XEM.and executes lines 4-15 while the burst is ongoing.T-XEM waits for the beginning of an idle time (line 5) and,then,monitors its duration (line 8).One of the following conditions may occur,i.e.:i) the idle time is longer thantTO(lines 9-10);or ii) a new TCP segment is received,or a new TCP ack becomes ready for transmission (lines11-12).In case ii) the detected idle time is clearly an interarrival time inside the ongoing burst.Therefore,T-XEMskips to line 5 and waits for the next idle time.In case i) (i.e,when a User Think Time is detected),T-XEMswitchesthe wireless interface off (line 16).The wireless interface remains off until a new burst is detected,i.e.,until anew request is generated by the application at the mobile host (line 17).At this point in time,T-XEM switches thewireless interface to PSM(line 2),and waits for the next idle time,as explained above (lines 4-15).7.3 XEMPerformance EvaluationIn this Section we exploit the analytical model derived in Section5to evaluate the improvements,in terms of energysaving,achieved by the Cross-Layer Energy Managers (A-XEMand T-XEM) with respect to the standard PSM.As faras T-XEM,the timeout value (i.e.,tTO) is deﬁned as tTO 2∙ RTT,where RTT denotes the (sampled) average valueof the Round Trip Time (RTT).The assumption behind this choice is that the probability of sampling a Round TripTime longer than twice the average value is negligible.Four RTT values are considered in the following analysis,i.e.,RTT=100 ms,200 ms,500 ms and 1 s,respectively.A ﬁrst set of experiments is aimed at evaluating the sensitiveness of the Cross-Layer Energy Managers withrespect to the User Think Time duration.Figure15(a) shows the energy consumption of T-XEM and A-XEM forincreasing UTTs (for T-XEM,a different curve is plotted for each RTT value).The energy consumption of PSM,derived fromderived fromEquation16,is also shown for comparison.The energy consumption of A-XEM(EAX) isconstant,and equal to toa∙ Pac.Finally,the energy consumption of T-XEMis as follows:ETX(UTT) =

EP(UTT) UTT ≤ tTOEP(tTO) +toa∙ PacUTT > tTO,(17)where EP(∙) is the PSM energy consumption.Equation17can be explained by recalling that the T-XEM lets thewireless interface in PSM for User Think Times shorter than tTO.Thus,in this range,the energy consumption ofPSM and T-XEM is exactly the same,i.e.EP(UTT).On the other hand,for User Think Times greater than tTO,T-XEMlets the wireless interface in PSMfor the ﬁrst tTOseconds,and,then,switches it to the off mode.As anticipated above,both A-XEM and T-XEM performworse than PSM for very short User Think Times.How-24ever,the region where this occurs is limited to very small User Think Times,in the order of few seconds.Ashighlighted in Section3,the probability of having such small UTTs is very low.Figure15(a) shows that,for typicalUTT values (tens of seconds),the Cross-Layer Energy Managers greatly outperform PSM.It is also interesting tocompare the performance of the two XEM implementations.Clearly,A-XEM exhibits the best performance.T-XEMconsumes 1.2 to 2.6 times the energy spent by A-XEM(for RTT equal to 0.1 s,and 1 s,respectively).To complete the analysis we now consider the energy consumed by the Cross-Layer Energy Managers not onlyduring User Think Times,but also within bursts.To this end,we assume that during bursts T-XEM never detectsfalse User Think Times,i.e.,we assume p (tiis a UTT|ti≥ tTO) = 1.Under this assumption,T-XEMbehaves exactlyas PSM during bursts (please note that the same property also holds for A-XEM).By exploiting the analyticalformulations of EP,EAXand ETX,we can evaluate the energy spent by PSM,A-XEM and T-XEM,respectively,during a single burst followed by a User Think Time.These quantities are throughout referred to as E(1)P,E(1)AXandE(1)TX.Accordingly,indexes R(E(1)AX,E(1)P) and R(E(1)TX,E(1)P) are evaluated and plotted in Figure15(b) for increasingUTTs.In Figure15(b) we only consider the lower and upper value for T-XEM,i.e.,0.1 s and 1 s.Furthermore,toinvestigate the performance of the Cross-Layer Energy Managers for a wide range of burst sizes,we considered bothshort (i.e.,a = 1) and long (i.e.,a = 100) burst sizes.For typical User Think Times,the improvement over PSM is quite evident.For example,for short burst sizes(i.e.,a=1),and a User Think Time of 30 s,A-XEM spends just 8.6% of the energy consumed by PSM,while T-XEM spends always less than 15% of the energy consumed by PSM.These values drop further to 4.4% and 7.7%,respectively,when the User Think Time increases to 60 s.As expected,the performance gains are reduced if wefocus on a particular User Think Time,and increase the burst sizes (e.g.,set a to 100).This is because,for a givenUser Think Time,the (energetic) cost of a burst (with respect to the cost of the User Think Time) increases with theburst size (see Figure12(b)).Since A-XEM and T-XEM differ from PSM in the way they handle User Think Times,the energy saved with respect to PSM is reduced when the burst size increases.In detail,for User Think Timesequal to 30 s and 60 s,the energy saved by the Cross-Layer Energy Managers with respect to PSMis about 20%and30%,respectively.It is also interesting to note that,as the average burst size increases,the performance differencebetween A-XEM and T-XEM becomes almost negligible,since for large bursts the energy consumed during burstsdominates the energy consumed during UTTs.This implies that for large bursts is not so important to consider verysophisticated algorithms to detect User Think Times.In conclusion,the above analysis has shown that Cross-Layer Energy Managers exhibit signiﬁcant improvements,in terms of energy saving,with respect to PSM.For typical values of the User Think Time (i.e,30 s),the additionalenergy saving is at least 20% (for large bursts),and can be as high as 91% (for small burst sizes).For larger UTTs(i.e.,60 s) the additional energy saving is at least 30%,and can be as high as 96%.To conclude the XEM analysis,we qualitatively compare it with STPM,BSD and SPSM.To this end,it is worthrecalling the index id,deﬁned in Section4as the ratio between the energy saved by PSM,and the energy saved byan ideal policy,that completely eliminates the energy consumption due to the wireless interface.id can be expressedas id = 1 −β −dfwhere β is the energy saved by PSM (relative to the wireless interface),d is the additional delayintroduced by PSM,and f is the ratio between the power consumption of the wireless interface and the rest of thedevice.If we focus on the download of a single page,followed by a UTT,d becomes the additional delay introducedby PSMon the complete cycle.Since the UTT length is usually quite larger than the download time,it is reasonableto assume d = 0 also in the PSM case (we already discussed in Section4that d = 0 applies also to the best casesof STPM,BSD and SPSM).d can be set to 0 also in the case of XEM,since it introduces just 100 ms to the PSMadditional delay.Therefore,the difference between XEM and the other techniques relies in the different values ofβ they are able to achieve.Figure16shows the range of XEM performance presented in Figure15(b),and themaximum expected performance of PSM,STPM,BSD and SPSM.Speciﬁcally,i) STPM behaves exactly like PSMduring a UTT;ii) BSD in the best case listens for a Beacon just every 900 ms,and sleeps for the rest of the time;iii)25%idd%XEM99.6%100%92%BSD/SPSMSTPM/PSM90%093%Figure 16:Comparison between XEM,PSM,STPM,BSD and SPSM.SPSM may be able to sleep for the whole UTT.XEM achieves higher energy saving because,unlike these policies,it exploits the off mode of the wireless interface during UTTs.Authors of [30] actually envision a similar BSDextension,but do not analyze it in detail.Authors of [1] deﬁne a STPM+ policy that is able to exploit also the offmode,but do not analyze it during UTTs.Anyway,XEM does not require MAC-level modiﬁcations (unlike BSD),and we believe that it is a simpler solution with respect to STPM+.7.4 Relaxing XEM assumptionsIn the deﬁnition of XEMwe have assumed that i) a single network application is running at the mobile host;ii) thisapplication does not open parallel TCP connections with the server;and iii) this application acts as a client,i.e.,new bursts start with a request sent by the mobile host to the (ﬁxed) server.All of these assumptions were aimedat simplifying the XEM deﬁnition and analysis.However,they can be easily relaxed with simple modiﬁcations toXEM.Let us start by focusing on assumption ii).A-XEM can be used unchanged even in the case of multiple TCPconnections.Actually,A-XEM detects User Think Times and new bursts generated by the application irrespectivelyof the number of TCP connection used.T-XEM could monitor the trafﬁc exchanged between the mobile host andthe Access Point,irrespectively of the particular TCP connections,to detect User Think Times.A User Think Timewould be detected when the the mobile and ﬁxed hosts do not exchange any data for tTOseconds.Good candidatevalues for tTOwould be calculated on the basis of self-learning algorithms,which have shown to be able to estimatethe statistical features of the joint trafﬁc produced by concurrent applications using parallel TCP connections (see[5] for details).It can be easily shown that this T-XEM modiﬁcation would allow us to relax assumption i) as well.Assumption i) could be relaxed also for A-XEM.Speciﬁcally,in the case of concurrent applications several instancesof the application-speciﬁc detection algorithms deﬁned by A-XEM would be concurrently operating on the mobilehost.A further A-XEM module,i.e.,a coordination module,would coordinate detections related to each speciﬁcapplication,and would be responsible for switching the wireless interface of the mobile host between the PSMandoff mode.Finally,XEM can be extended to relax the third assumption as well,and support mobile hosts acting as servers(i.e.,able to receive asynchronous requests from the Internet).To this end,XEM would periodically switch thewireless interface of the mobile host to PSM during User Think Times.This way,frames that could have beenbuffered at the Access Point would be downloaded by exploiting the PSM mechanisms.Furthermore,XEM wouldswitch again the wireless interface off if no new data are exchanged for a Beacon Interval.Obviously,this XEMextension would have some additional energetic cost,since more switching-on events are required,and more timewould be spent by the wireless interface in PSM.8 Summary and ConclusionsIn this paper we have extensively evaluated the performance of the 802.11 PSM in terms of energy consumptionas a function of a number of application and network parameters,and as a function of the MAC-level congestion.26The main results can be summarized as follows.During trafﬁc bursts PSM is quite effective,and able to save up to90% of the energy spent without energy management.It works remarkably well for a wide range of of burst sizes.Furthermore,it is able to signiﬁcantly reduce the negative effect on energy consumption of low transport-levelthroughput and MAC-level contention.Unfortunately,PSM is not very ﬁt to deal with User Think Times betweenbursts.We have shown that this originates from the fact that PSM switches the wireless interface to the sleepmode during any type of idle time.During long idle times,such as UTTs,switching it to the off mode provesto be more energy efﬁcient.Therefore,we have proposed and evaluated XEM,a Cross-Layer Energy Managerthat uses PSM during bursts,and switches the wireless interface off during UTTs.XEM implements very simpleyet efﬁcient algorithms to detect the beginning of bursts and UTTs,without requiring any modiﬁcation to legacy-Internet applications or to the standard 802.11.XEMis able to achieve energy saving between 20% and 96% withrespect to the standard PSM.Our opinion is that these improvements stem from the cross-layer nature of the XEM design.Speciﬁcally,PSMjust uses MAC-level information (i.e.,availability of frames to/from the mobile host) to detect idle times,andmanages the mobile host’s wireless interface accordingly.By operating exclusively at the MAC level,PSM is notﬂexible enough to cope with the network trafﬁc generated by typical Internet applications in Wi-Fi hotspots.Inparticular,PSM is not able to distinguish between short idle times (within bursts) and long idle times (betweenconsecutive bursts),and is thus not able to dynamically select the best energy-saving policy.On the other hand,XEM dynamically chooses between sleep-based and off-based policies,according to the type of idle time that is oc-curring.Furthermore,the algorithms it uses to detect idle times (and distinguish between different idle-time types),exploit information residing at different layers in the protocol stack,fromthe MAC up to the application layer.Theperformance improvements presented in this paper show that such a cross-layer approach is very promising.The main contribution of this paper is thus twofold.To the best of our 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